Tag: ili2010

I’ve demonstrated previously a way of using JQuery to pull the JSON (Javascript Object Notion) output of a Yahoo pipe – a Javascript version of an RSS feed, essentially – into a web page (Previewing the Contents of a JSON Feed in Yahoo Pipes), but if the idea of tinkering with JQuery is one step towards coding hell to far, what are the alternatives?

One approach is to grab the RSS output of a pipe and use it as the RSS URL in Feed2js, which will let you customise a review display of the output of the pipe and give you a handy Javascript embed code for the display that you can embed in your own page.

I don’t know why, but I keep forgetting the Yahoo pipes itself offers a couple of widgets – referred to as “badges” – for displaying the output of a pipe in your own web page: Yahoo Pipes Badges how to.

As with the pipe output previews on the “homepage” of a Yahoo Pipe, three sorts of display are possible – a list based display, an image display (which displays a slide show of images identified as such in the feed) and a map badge, which renders markers on an interactive Yahoo map. For the latter, you can also take the KML output of a feed, paste it into the Google Maps search box, and grab an iframe embed code.

Unfortunately, embedding the Javascript snippets used to display the Yahoo Pipes badges in WordPress.com hosted blogs isn’t allowed – so to display pipe fed content in such a blog you need to use the RSS output URL from a pipe in a WordPress RSS sidebar widget.

Picking up on @briankelly’s Thoughts on ILI 2010, where he reports on a few gross level stats about #ili2010 hashtag activity grabbed from Summarizr, here are a few things I observed from looking at some of the hashtag community network stats…

To start with, I looked at the “inner hashtag community” where I grab the list of hashtaggers and their friends who have also used the hashtag and make links between them to give this sort of graph, as used before in many OUseful.info posts:

(Directed graph from person to friend (i.e. to person they follow); node size proportional to in-degree, heat to out-degree.)

After running a few network statistics generated using Gephi, and exporting the data from the Gephi Data Table view, I uploaded the statistics data to IBM’s ManyEyes site here. This allows us to view the distribution of the hashtaggers based on various statistical and network measures using a range of other visualisation techniques, such as histograms (view interactive histogram chart for ILI2010 hashtaggers, interactive scatterplot)

So for example, here’s the distribution of hashtaggers by total number of followers (that is, including followers outside the hashtag community) as a histogram:

If we look at the betweenness measure, which was calculated over the friends connections between the hashtaggers, we can see who’s best suited to getting a message broadcast across the community through direct and friend-of-a-friend links:

If we look at the in-degree (number of people in the hashtag community who have friended (i.e. are following) an individual, divided by the total number of friends of that individual, we can identify people who are being followed by more people in the community than they have as friends:

If we look at the in-degree divided by a users total number of followers, we can see the extent to which a person’s twitter feed is dominated by updates from folk who have used the ILI2010 hashtag:

In the above case, we see one person who appears to only follow members of the ILI2010 hashtag community. (I’m guessing that if folk come to twitter through a conference, this might be a signature of that?) Before you get too excited though, a little more digging suggests that that person only follows 1 person;-)

The interactive scatterplot allows us to view 3 dimensions of data – in the following case, ‘m looking for well connected (good betweenness centrality), well respected (high in-degree) folk in the hashtag community who also have a large reach in terms of their total number of followers:

In terms of audience development, we can also create a network based on the complete follower lists of the ILI2010 hashtaggers. Creating such a graph generates a network with 71627 nodes, of which 236 were hashtaggers – meaning that in principle 71,391 people outside the hashtag community might have seen an ILI2010 hashtagged tweet…

Using a directed graph from hashtaggers to their followers, If we filter the graph to only show individuals with an in-degree above 60, say, we can see those people who are following at least 60 people who have used the hashtag:

In the way I have constructed this graph, the nodes showing Twitter usernames are in the hashtag community, the numerical IDs are individuals who didn’t use the ILI2010 hashtag but who do follow at least 60 people who did, and therefore presumably saw quite a lot of tweets about the event.

Looking up the twitter IDs of the “friends of the hashtag community”, we see the following people did not use the hashtag over the sample period, but do follow lots of people who did: @ijclark, @aekins, @metalibrarian, @schammond, @Jo_Bo_Anderson, @research_inform, @tomroper, @facetpublishing, @DavidGurteen

Of course, to know the extent to which hashtagger activity dominates the twitterstream of this “friends of the ahshtag community”, we’d need to normalise this against their total number of friends; because for exampe If I follow 20k people, of which 60 were hashtaggers, I’d probably miss most of the hashtagged tweets; whereas, if I follow 100 people, of which 60 are hashtaggers, the density of tweets received from hashtaggers could be expected to be quite high.

Okay – enough for now… although if you can think of anything else that might be interesting to know about the wider community around the hashtaggers, please post it in a comment below:-)